How to integrate Honeyhive MCP with Pydantic AI

This guide walks you through connecting Honeyhive to Pydantic AI using the Composio tool router. By the end, you'll have a working Honeyhive agent that can add new datapoints to your evaluation dataset, list all datasets in your honeyhive project, log a batch of model events for analysis through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Honeyhive account through Composio's Honeyhive MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Honeyhive

Api Key

Honeyhive is an AI observability and evaluation platform for analyzing LLM apps. It helps teams monitor, debug, and improve AI system reliability faster.

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Introduction

This guide will help you understand how to give your Pydantic AI agent real control over a Honeyhive account through Composio's Honeyhive MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Honeyhive with

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI LlamaIndex CrewAI

TL;DR

Here's what you'll learn:

How to set up your Composio API key and User ID
How to create a Composio Tool Router session for Honeyhive
How to attach an MCP Server to a Pydantic AI agent
How to stream responses and maintain chat history
How to build a simple REPL-style chat interface to test your Honeyhive workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

Type Safety: Built on Pydantic for automatic data validation
MCP Support: Native support for Model Context Protocol servers
Streaming: Built-in support for streaming responses
Async First: Designed for async/await patterns

What is the Honeyhive MCP server, and what's possible with it?

The Honeyhive MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Honeyhive account. It provides structured and secure access to your AI observability platform, so your agent can perform actions like managing datasets, logging model and tool events, evaluating runs, and configuring project settings on your behalf.

Dataset management and organization: Create, retrieve, and delete datasets for your AI projects, helping you maintain organized and up-to-date evaluation data.
Efficient event logging: Log batches of model or external tool events, enabling comprehensive tracking and analysis of AI system interactions in real-time.
Data curation and cleanup: Add new datapoints to datasets or remove specific datapoints, ensuring your evaluation data remains accurate and relevant.
Streamlined evaluation workflows: Mark evaluation runs as completed and fetch project configuration details, making it easy to track progress and update run statuses automatically.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS

Prerequisites

Before starting, make sure you have:

Python 3.9 or higher
A Composio account with an active API key
Basic familiarity with Python and async programming

Getting API Keys for OpenAI and Composio

OpenAI API Key

Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
Keep the API key safe.

Composio API Key

Log in to the Composio dashboard.
Navigate to your API settings and generate a new API key.
Store this key securely as you'll need it for authentication.

Install dependencies

bash

pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

composio connects your agent to external SaaS tools like Honeyhive
pydantic-ai lets you create structured AI agents with tool support
python-dotenv loads your environment variables securely from a .env file

Set up environment variables

bash

COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

COMPOSIO_API_KEY authenticates your agent to Composio's API
USER_ID associates your session with your account for secure tool access
OPENAI_API_KEY to access OpenAI LLMs

Import dependencies

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

What's happening:

We load environment variables and import required modules
Composio manages connections to Honeyhive
MCPServerStreamableHTTP connects to the Honeyhive MCP server endpoint
Agent from Pydantic AI lets you define and run the AI assistant

Create a Tool Router Session

python

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Honeyhive
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["honeyhive"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

What's happening:

We're creating a Tool Router session that gives your agent access to Honeyhive tools
The create method takes the user ID and specifies which toolkits should be available
The returned session.mcp.url is the MCP server URL that your agent will use

Initialize the Pydantic AI Agent

python

# Attach the MCP server to a Pydantic AI Agent
honeyhive_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[honeyhive_mcp],
    instructions=(
        "You are a Honeyhive assistant. Use Honeyhive tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)

What's happening:

The MCP client connects to the Honeyhive endpoint
The agent uses GPT-5 to interpret user commands and perform Honeyhive operations
The instructions field defines the agent's role and behavior

Build the chat interface

python

# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Honeyhive.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()

What's happening:

The agent reads input from the terminal and streams its response
Honeyhive API calls happen automatically under the hood
The model keeps conversation history to maintain context across turns

Run the application

python

if __name__ == "__main__":
    asyncio.run(main())

What's happening:

The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Honeyhive and Pydantic AI:

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Honeyhive
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["honeyhive"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    honeyhive_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[honeyhive_mcp],
        instructions=(
            "You are a Honeyhive assistant. Use Honeyhive tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Honeyhive.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Honeyhive through Composio's Tool Router. With this setup, your agent can perform real Honeyhive actions through natural language. You can extend this further by:

Adding other toolkits like Gmail, HubSpot, or Salesforce
Building a web-based chat interface around this agent
Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Honeyhive for workflow automation)

This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

TOOLS

Supported Tools

Every Honeyhive action and event your agent gets out of the box.

Add datapoints to dataset

Tool to add datapoints to a dataset.

Compare Experiment Runs

Tool to retrieve experiment comparison between two evaluation runs.

Compare Runs Events

Tool to compare events between two experiment runs side-by-side.

Batch Create Datapoints

Tool to create multiple datapoints in a single batch operation.

Create Batch Model Events

Tool to create multiple model events in a single request.

Create Batch Tool Events

Tool to log a batch of external API calls as tool events.

Create Configuration

Creates a new configuration in HoneyHive for managing LLM or pipeline settings.

Create Datapoint

Tool to create a new datapoint with input-output pairs.

Create Dataset

Tool to create a dataset.

Create Event

Tool to create a new event in HoneyHive to track execution of different parts of your application.

Create Metric

Tool to create a new metric in HoneyHive.

Create Model Event

Tool to create a new model event to log LLM call data.

Create Tool

Creates a new tool definition in a HoneyHive project.

Delete Datapoint

Tool to delete a specific datapoint by its ID.

Delete Dataset

Tool to delete a dataset by ID.

End Evaluation Run

Tool to update an evaluation run's status and metadata.

Get Configurations

Tool to retrieve a list of configurations.

Get Datasets

Retrieve datasets from HoneyHive for a specified project.

Get Events

Tool to query events with filters and projections from HoneyHive.

Get Events By Session ID

Tool to retrieve the complete tree of nested events for a specific session.

Get Events Chart

Tool to retrieve charting and analytics data for events over time.

Get Metrics

Retrieves all metrics associated with a HoneyHive project.

Get Projects

Tool to retrieve all projects in the HoneyHive account.

Get Evaluation Run Details

Tool to get details of an evaluation run by its UUID.

Get Run Metrics

Tool to get event metrics for an experiment run.

Get Evaluation Runs

Tool to retrieve a list of evaluation runs from HoneyHive.

Get Runs Schema

Tool to retrieve the schema for experiment runs in HoneyHive.

Get Session

Retrieve a complete session tree by session ID from HoneyHive.

List Tools

Tool to list all available Honeyhive tools.

Retrieve Datapoint

Retrieve a specific datapoint by its ID from HoneyHive.

Retrieve Datapoints

Retrieve datapoints from a HoneyHive project.

Retrieve Events

Retrieve and export events from a HoneyHive project.

Retrieve Experiment Result

Tool to retrieve the result of a specific experiment run.

Start Evaluation Run

Creates a new evaluation run to group and track multiple session events for analysis.

Start Session

Start a new HoneyHive session for tracing and observability.

Update Configuration

Tool to update an existing HoneyHive configuration.

Update Datapoint

Update an existing datapoint by ID.

Update Dataset

Tool to update an existing dataset.

Update Event

Update an existing HoneyHive event by ID.

Update Metric

Tool to update an existing metric.

Update Project

Updates an existing HoneyHive project's name or description.

Update Tool

Tool to update an existing tool in HoneyHive.

FRAMEWORKS

How to build Honeyhive MCP Agent with another framework

OpenAI Agents SDK

Use Honeyhive MCP with OpenAI Agents SDK

Claude Agent SDK

Use Honeyhive MCP with Claude Agent SDK

Claude Code

Use Honeyhive MCP with Claude Code

Claude Cowork

Use Honeyhive MCP with Claude Cowork

Codex

Use Honeyhive MCP with Codex

OpenClaw

Use Honeyhive MCP with OpenClaw

Hermes

Use Honeyhive MCP with Hermes

CLI

Use Honeyhive MCP with CLI

Google ADK

Use Honeyhive MCP with Google ADK

LangChain

Use Honeyhive MCP with LangChain

Vercel AI SDK

Use Honeyhive MCP with Vercel AI SDK

Mastra AI

Use Honeyhive MCP with Mastra AI

LlamaIndex

Use Honeyhive MCP with LlamaIndex

CrewAI

Use Honeyhive MCP with CrewAI

MORE TOOLKITS

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Toolkit marketplace

Composio

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Composio search

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Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service.

Perplexityai

Api Key

Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows.

Browser tool

No Auth

Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.

FAQ

Frequently asked questions

With a standalone Honeyhive MCP server, the agents and LLMs can only access a fixed set of Honeyhive tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Honeyhive and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Pydantic AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Honeyhive tools.

Yes, absolutely. You can configure which Honeyhive scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Honeyhive data and credentials are handled as safely as possible.

Start with Honeyhive.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Honeyhive tool your agent needs.Free to start.

Start building

Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREE BOOK A DEMO

Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Honeyhive MCP with Pydantic AI

Connect Honeyhive without auth hassles

Introduction

Also integrate Honeyhive with

TL;DR

What is Pydantic AI?

What is the Honeyhive MCP server, and what's possible with it?

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies

Create a Tool Router Session

Initialize the Pydantic AI Agent

Build the chat interface

Run the application

Complete Code

Conclusion

Supported Tools

How to build Honeyhive MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

Explore Other Toolkits

Composio

Composio search

Perplexityai

Browser tool

Frequently asked questions

What are the differences in Tool Router MCP and Honeyhive MCP?+

Can I use Tool Router MCP with Pydantic AI?+

Can I manage the permissions and scopes for Honeyhive while using Tool Router?+

How safe is my data with Composio Tool Router?+

Start with Honeyhive.It takes 30 seconds.

Teams ship real agents on Composio